Saturday, November 10, 2012

NIPS 2012 Papers on Online Learning and Convex Optimization

Here is the list of interesting papers accepted in NIPS 2012 related to Online Learning and Convex Optimization:

A. Online Learning

- No-Regret Algorithms for Unconstrained Online Convex Optimization
- Mirror Descent Meets Fixed Share (and feels no regret)
- Relax and Randomize : From Value to Algorithms
- Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence
- Confusion-Based Online Learning and a Passive-Aggressive Scheme
- Risk-Aversion in Multi-armed Bandits
- Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions

B. Convex Optimization

- Optimal Regularized Dual Averaging Methods for Stochastic Optimization
- Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form
- Query Complexity of Derivative-Free Optimization
- Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
- A quasi-Newton Proximal Splitting Method
- Stochastic Gradient Descent with Only One Projection
- A Stochastic Gradient Method with an Exponential Convergence 
Rate with Finite Training Sets
- Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization
- Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods
- Feature Clustering for Accelerating Parallel Coordinate Descent

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